Tensorized LSTM with adaptive shared memory for learning trends in multivariate time series

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United States of America Patent

PATENT NO 11783181
APP PUB NO 20210064998A1
SERIAL NO

16987789

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A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.

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  • NEC CORPORATION

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Chen, Haifeng West Windsor, US 306 1485
Cheng, Wei Princeton Junction, US 242 425
Ni, Jingchao Princeton, US 35 15
Xu, Dongkuan State College, US 14 6
Yu, Wenchao Plainsboro, US 42 22

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